提出多视角结构卷积网络，提升自动驾驶点云识别的跨域鲁棒性。
Multi-view Structural Convolution Network for Domain-Invariant Point Cloud Recognition of Autonomous Vehicles
- 设计结构卷积与聚合层，捕捉点云局部与全局几何特征。
- 在跨域测试中平均准确率达82.0%，比PointTransformer高15.8%。
- 适合自动驾驶场景下点云数据分布变化大的应用需求。
点云表征已成为计算机视觉领域的研究热点，并被应用于自动驾驶。然而，由于数据集和传感器技术的差异，将深度学习模型用于点云识别面临挑战。为此，本文提出多视角结构卷积网络（MSCN），用于实现域不变的点云识别。MSCN包含结构卷积层（SCL），用于提取点云的局部上下文几何特征；以及结构聚合层（SAL），用于提取并聚合局部与整体上下文特征。此外，通过在源域生成的未见域点云上训练，增强特征鲁棒性，使模型获得域不变表示。大量跨域实验表明，MSCN平均准确率达到82.0%，相比强基线PointTransformer提升15.8%，验证了其在真实域偏移下的有效性。代码已公开于https://github.com/MLMLab/MSCN。
原文摘要 · Abstract (English)
Point cloud representation has recently become a research hotspot in the field of computer vision and has been utilized for autonomous vehicles. However, adapting deep learning networks for point cloud data recognition is challenging due to the variability in datasets and sensor technologies. This variability underscores the necessity for adaptive techniques to maintain accuracy under different conditions. In this paper, we present the Multi-View Structural Convolution Network (MSCN) designed for domain-invariant point cloud recognition. MSCN comprises Structural Convolution Layers (SCL) that extract local context geometric features from point clouds and Structural Aggregation Layers (SAL) that extract and aggregate both local and overall context features from point clouds. Furthermore, MSCN enhances feature robustness by training with unseen domain point clouds generated from the source domain, enabling the model to acquire domain-invariant representations. Extensive cross-domain experiments demonstrate that MSCN achieves an average accuracy of 82.0%, surpassing the strong baseline PointTransformer by 15.8%, confirming its effectiveness under real-world domain shifts. Our code is available at https://github.com/MLMLab/MSCN.
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